NDLI: Target classification by autoregressive modeling using range extent profiles

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Target classification by autoregressive modeling using range extent profiles

Content Provider	IEEE Xplore Digital Library
Author	Mallick, M.K. Coraluppi, S.
Copyright Year	2000
Description	Author affiliation: Alphatech Inc., Burlington, MA, USA (Mallick, M.K.; Coraluppi, S.)
Abstract	We present a novel algorithm based on autoregressive (AR) modeling for analyzing the separability and classification of ground targets using range extent data. Given a range extent profile of a target, we estimate the appropriate model order for the data using the Akaike information criterion (AIC). This prevents under-filling or over-fitting of the data. Previous researchers have shown that even if the actual process is not an AR process, the AR model serves as a reasonable model for a wide class of practical problems. Error modeling for the range extent data is extremely difficult due to the complex nature of the scattering process, uncertainties in the channel, sensor state, target dynamics, and estimation of the range extent from a range profile. Therefore, our data-driven approach provides a useful algorithm for analyzing target model separability and classification. We apply the algorithm to simulated range extent data and obtain good classification results. We plan to test the algorithm further with real range extent data.
File Size	516652
File Format	PDF
ISBN	2725700000
DOI	10.1109/IFIC.2000.862447
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-07-10
Access Restriction	Subscribed
Rights Holder	Int. Soc. Inf. Fusion
Subject Keyword	Target tracking Radar scattering Radar tracking State estimation Signal processing algorithms Synthetic aperture radar Kinematics Uncertainty Testing Target recognition
Content Type	Text
Resource Type	Article

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